Simulating Tissues with Oneiric
Patrick Martin
Introduction
library(oneiric)
library(scater)
library(ggplot2)
library(RColorBrewer)
set.seed(288)Data
Chaos Parameters
data(oneiric)
str(map_params)## num [1:36, 1:6] -1.452 -0.983 1.536 -0.91 2.613 ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:6] "a" "b" "c" "d" ...Simulated Territories
sim_ter <- paste0(system.file(package = "oneiric"), "/inst/extdata/")
dir(sim_ter)## character(0)Preparing output directory
if (!dir.exists("output/")) {
output <- dir.create("output/")
}
output <- "output/"Creating New Data sets
Simulate New Territories
circle <- simulate_spatial(n_cells = 6000,
n_territories = 5,
n_samples = 12,
pattern = "circle",
max_expanse = 0.3)
rod <- simulate_spatial(n_cells = 6000,
n_territories = 5,
n_samples = 12,
pattern = "rod",
max_width = 0.1,
max_length = 0.5)
chaos_map <- simulate_spatial(n_cells = 6000,
n_samples = 12,
pattern = "chaos",
max_expanse = 0.02)Circular Territories
circles <- do.call("rbind", circle)
circles$Territory <- as.factor(circles$Territory)
g <- ggplot(circles, aes(x = x, y = y, col = Territory)) +
geom_point(size = 0.5) +
theme_bw() +
scale_color_brewer(palette = "Spectral") +
facet_wrap(~sample) +
guides(colour = guide_legend(
override.aes = list(size = 5)))
print(g)
Circular Territories
Rod Territories
rods <- do.call("rbind", rod)
rods$Territory <- as.factor(rods$Territory)
g <- ggplot(rods, aes(x = x, y = y, col = Territory)) +
geom_point(size = 0.5) +
theme_bw() +
scale_color_brewer(palette = "Spectral") +
facet_wrap(~sample) +
guides(colour = guide_legend(
override.aes = list(size = 5)))
print(g)
Rod Territories
Tinkerbell Map Territories
chaos <- do.call("rbind", chaos_map)
chaos$Territory <- as.factor(chaos$Territory)
g <- ggplot(chaos, aes(x = x, y = y, col = Territory)) +
geom_point(size = 0.5) +
theme_bw() +
scale_color_brewer(palette = "Spectral") +
facet_wrap(~sample) +
guides(colour = guide_legend(
override.aes = list(size = 5)))
print(g)
Tinkerbell Map Territories
Simulate New Cells
circle_counts <- simulate_cells(circle,
cell_composition = 2,
n_genes = 2000,
seed = 1729)## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!rod_counts <- simulate_cells(rod,
cell_composition = 2,
n_genes = 2000,
seed = 1729)## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!chaos_counts <- simulate_cells(chaos_map,
cell_composition = 2,
n_genes = 2000,
seed = 1729)## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!## Getting parameters...## Creating simulation object...## Simulating library sizes...## Simulating gene means...## Simulating group DE...## Simulating cell means...## Simulating BCV...## Simulating counts...## Simulating dropout (if needed)...## Sparsifying assays...## Automatically converting to sparse matrices, threshold = 0.95## Skipping 'BatchCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BaseCellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'BCV': estimated sparse size 1.5 * dense matrix## Skipping 'CellMeans': estimated sparse size 1.5 * dense matrix## Skipping 'TrueCounts': estimated sparse size 2.4 * dense matrix## Skipping 'counts': estimated sparse size 2.4 * dense matrix## Done!Export Data sets
export_simulation(spatial = circle,
cells = circle_counts,
out_dir = output,
file_tag = "circle_spatial_territories")
export_simulation(spatial = rod,
cells = rod_counts,
out_dir = output,
file_tag = "circle_spatial_territories")
export_simulation(spatial = choas_map,
cells = chaos_counts,
out_dir = output,
file_tag = "circle_spatial_territories")